Load in Initial Processing file (and dependencies)
source("InitialProcessing.R")
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.7
✓ tidyr 1.1.4 ✓ stringr 1.4.0
✓ readr 2.0.0 ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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* `` -> ...1
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chr (1): ...1
dbl (96): 3-1-B-0-2_R1_filt.fastq.gz, 3-1-B-1-2_R1_filt.fastq.gz, 3-1-B-180_R1_filt.fastq.gz, 3-1-B-2...
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* `` -> ...1
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── Column specification ─────────────────────────────────────────────────────────────────────────────────
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chr (7): ...1, Kingdom, Phylum, Class, Order, Family, Genus
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Rows: 192 Columns: 16
── Column specification ─────────────────────────────────────────────────────────────────────────────────
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chr (13): Files, ID, Sample, Depth, Type, Paths, RenFiles, RenPaths, TrimmedFiles, TrimmedPaths, Filt...
dbl (3): ReadDir, Station, Size
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Rows: 91 Columns: 7
── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Depth
dbl (6): Station, Size_Class, Bin_Size, DNAperLiter, MassperLiter, ParticlesPerLiter
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Rows: 96 Columns: 5
── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): ID, Depth, Flag
dbl (2): Station, Size_Class
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extract_numeric() is deprecated: please use readr::parse_number() instead
Joining, by = "TagLevel"
`summarise()` has grouped output by 'ID'. You can override using the `.groups` argument.
Warning: Removed 15 rows containing missing values (position_stack).
`summarise()` has grouped output by 'nASV'. You can override using the `.groups` argument.
Rows: 9 Columns: 7
── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Station
dbl (6): lat, long, UTMX, UTMY, depth, do
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
source("ChesapeakePersonalLibrary.R")
# my_sizes <- sort(unique(microbialAbundance$Size_Class))
# my_sizes
# scientific_10_c <- function(x) {
# xout <- gsub("1e", "10^{", format(x),fixed=TRUE)
# xout <- gsub("{-0", "{-", xout,fixed=TRUE)
# xout <- gsub("{+", "{", xout,fixed=TRUE)
# xout <- gsub("{0", "{", xout,fixed=TRUE)
# xout <- paste(xout,"}",sep="")
# return(parse(text=xout))
#
# }
#
# scale_y_log10nice <- function(name=NULL,omag=seq(-10,20),...) {
# breaks10 <- 10^omag
# scale_y_log10(breaks=breaks10,labels=scientific_10_c(breaks10),...)
# } # Reclocated to ChesapeakePersonalLibrary.R
abunPlot <- microbialAbundance %>%
filter(is.finite(MassperLiter)) %>%
filter(Depth %in% c("Surface", "Bottom")) %>%
mutate(copiesPerMg = copiesPerL / MassperLiter) %>%
ggplot(aes(x = Size_Class, y = copiesPerMg, shape = as.factor(Station), fill = as.factor(Station))) +
facet_wrap(~Depth, ncol = 1) +
theme_bw(base_size = 16) +
geom_point(size = 4) +
#geom_path() +
geom_path(aes(color = as.factor(Station))) +
scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
scale_y_log10nice() +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
labs(y = "16s + 18s Genes / mg Particles", x = expression(paste("Particle Size (", mu, "m)"))) +
theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = .5),
axis.title.y = element_text(margin = unit(c(0, 0, 0, 0), "mm"), vjust = 0))
abunPlot
PPLPlot <- microbialAbundance %>%
filter(Size_Class >= 1) %>%
filter(Depth %in% c("Surface", "Bottom")) %>%
ggplot(aes(x = Size_Class, y = ParticlesPerLiter, shape = as.factor(Station), fill = as.factor(Station))) +
facet_wrap(~Depth, ncol = 1) +
theme_bw(base_size = 16) +
geom_point(size = 4) +
#geom_path() +
geom_path(aes(color = as.factor(Station))) +
scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
scale_y_log10nice(omag=seq(-10,20, by = 2)) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
labs(y = "Particles/L/mm", x = expression(paste("Particle Size (", mu, "m)"))) +
theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = .5),
axis.title.y = element_text(margin = unit(c(0, 0, 0, 0), "mm"), vjust = 0))
PPLPlot
ParticleMassPlot <- microbialAbundance %>%
filter(Size_Class >= 1) %>%
filter(Depth %in% c("Surface", "Bottom")) %>%
ggplot(aes(x = Size_Class, y = MassperLiter/ParticlesPerLiter, shape = as.factor(Station), fill = as.factor(Station))) +
facet_wrap(~Depth, ncol = 1) +
theme_bw(base_size = 16) +
geom_point(size = 4) +
#geom_path() +
geom_path(aes(color = as.factor(Station))) +
scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
scale_y_log10nice(omag = seq(-10, 20, by = 2)) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
labs(y = "Dry Mass (mg) / Particle", x = expression(paste("Particle Size (", mu, "m)"))) +
theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = .5),
axis.title.y = element_text(margin = unit(c(0, 0, 0, 0), "mm"), vjust = .5))
ParticleMassPlot
I need to load over data from the CB-Bay project
source("CBMap.R")
Rows: 9 Columns: 7
── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Station
dbl (6): lat, long, UTMX, UTMY, depth, do
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(cowplot)
library(grid)
library(gridExtra)
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
x.grob <- textGrob(expression(paste("Particle Size (", mu, "m)")),
gp=gpar(fontsize=16))
combinedPlot <- plot_grid(PPLPlot, ParticleMassPlot, abunPlot, nrow = 1, labels = c("B", "C", "D"))
meta2 <- grid.arrange(arrangeGrob(combinedPlot, bottom = x.grob))
cbMap
physical_particles <- plot_grid(cbMap, meta2, rel_widths = c(1,3), labels = c("A", ""))
physical_particles
ggsave("PhysicalParticles.png", physical_particles, height = 4, width = 8)
What are the most abundant ASVs from each cluster? Plot their abundance. Pick the prettiest few and plot
source("ClusteringCore.R")
hgroups_exemplars <- cutree(sclust, k = 10)
nonSpikes20C <- nonSpikes20 %>% left_join(
tibble(ASV = names(hgroups_exemplars), cluster = hgroups_exemplars), by = "ASV")
ASVsToView <- nonSpikes20C %>%
filter(Kingdom == "Bacteria") %>%
group_by(ASV, cluster) %>%
summarise(meanCopies = mean(copiesPerL)) %>%
group_by(cluster) %>%
summarize(topASV = ASV[which.max(meanCopies)])
`summarise()` has grouped output by 'ASV'. You can override using the `.groups` argument.
ASVsToView
nonSpikes20C %>%
right_join(ASVsToView, by = c("ASV" = "topASV", "cluster")) %>%
group_by(ASV) %>%
slice_head()
nonSpikes20C %>%
right_join(ASVsToView, by = c("ASV" = "topASV", "cluster" = "cluster")) %>%
filter(cluster <= 7, Depth %in% c("Surface", "Bottom"), !is.na(Depth)) %>%
arrange(Size_Class) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class, fill = as.factor(Station), shape = as.factor(Station))) +
scale_y_log10nice() + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Depth ~ Tag) +
geom_point(size = 2) +
geom_path(aes(color = as.factor(Station))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma")
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
Fig X. Copies per L
nonSpikes20C %>%
right_join(ASVsToView, by = c("ASV" = "topASV", "cluster" = "cluster")) %>%
filter(cluster <= 7, Depth %in% c("Surface", "Bottom"), !is.na(Depth)) %>%
filter(!is.na(MassperLiter)) %>%
arrange(Size_Class) %>%
ggplot(aes(y = copiesPerL/MassperLiter, x = Size_Class, fill = as.factor(Station), shape = as.factor(Station))) +
scale_y_log10nice() + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Depth ~ Tag) +
geom_point(size = 2) +
geom_path(aes(color = as.factor(Station))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma")
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
Fig X + 1. Copies per mg stuff. I don’t love these examples because there is a bunch of station to station ariance, and you never see things that like, always show up at large stations.
I wonder if I can find some good examples of consistantly large particle stuff or something.
I look for things that have high positive slope coefficients between size and normalized copies per L. And select for high mean abundance and high slope. And use site as a random effect? Would be nice to have some things that are intermediately high though. Some sort of polynomial regression?
nest20 <- nonSpikes20 %>%
select(ASV, Station:ParticlesPerLiter, copiesPerL) %>%
mutate(Station = as.factor(Station)) %>%
group_by(ASV) %>%
nest()
TestSub <- nest20[[2]][[1]] %>% ungroup()
TestSub
library(lme4)
Loading required package: Matrix
Attaching package: ‘Matrix’
The following objects are masked from ‘package:tidyr’:
expand, pack, unpack
library(broom)
library(broom.mixed)
lm(log(copiesPerL/Bin_Size) ~ log(Size_Class), data = TestSub) %>% tidy()
lmer(log(copiesPerL/Bin_Size) ~ log(Size_Class) + (Size_Class|Station) + (Size_Class|Depth) , data = TestSub %>%
mutate(as.factor(Station))) %>% AIC()
boundary (singular) fit: see ?isSingular
[1] 294.8901
lmer(log(copiesPerL/Bin_Size) ~ log(Size_Class) + (1|Station) + (1|Depth) , data = TestSub %>%
mutate(as.factor(Station))) %>% AIC() # Better
[1] 284.1189
modDf <- nest20 %>%
mutate(mod = map(data,
~lmer(log((copiesPerL+1)/Bin_Size) ~ log(Size_Class) +
(Size_Class|Station) + (Size_Class|Depth), data = .)
)
)
boundary (singular) fit: see ?isSingular
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Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 147: ASV = "ASV_1502".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 147: ASV = "ASV_1502".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
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Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 214: ASV = "ASV_186".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 214: ASV = "ASV_186".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
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Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 230: ASV = "ASV_197".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 1 negative eigenvalues
ℹ The warning occurred in group 230: ASV = "ASV_197".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
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Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 239: ASV = "ASV_2015".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 239: ASV = "ASV_2015".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
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boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 275: ASV = "ASV_2299".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 275: ASV = "ASV_2299".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 324: ASV = "ASV_280".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 324: ASV = "ASV_280".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 419: ASV = "ASV_407".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 419: ASV = "ASV_407".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge with max|grad| = 2.37793 (tol = 0.002, component 1)
ℹ The warning occurred in group 458: ASV = "ASV_468".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
ℹ The warning occurred in group 458: ASV = "ASV_468".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 478: ASV = "ASV_5".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 478: ASV = "ASV_5".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 566: ASV = "ASV_657".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 1 negative eigenvalues
ℹ The warning occurred in group 566: ASV = "ASV_657".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 595: ASV = "ASV_71".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 1 negative eigenvalues
ℹ The warning occurred in group 595: ASV = "ASV_71".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge with max|grad| = 1.37981 (tol = 0.002, component 1)
ℹ The warning occurred in group 608: ASV = "ASV_730".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
ℹ The warning occurred in group 608: ASV = "ASV_730".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 635: ASV = "ASV_789".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 1 negative eigenvalues
ℹ The warning occurred in group 635: ASV = "ASV_789".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 648: ASV = "ASV_817".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 648: ASV = "ASV_817".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ unable to evaluate scaled gradient
ℹ The warning occurred in group 705: ASV = "ASV_94".
Warning: Problem with `mutate()` column `mod`.
ℹ `mod = map(...)`.
ℹ Model failed to converge: degenerate Hessian with 2 negative eigenvalues
ℹ The warning occurred in group 705: ASV = "ASV_94".
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
modDf01 <- modDf %>%
mutate(tidied = map(mod, tidy)) %>%
unnest(tidied) %>%
select(-data, -mod)
modDf02 <- modDf01 %>%
filter(term == "log(Size_Class)") %>%
select(ASV, estimate, std.error)
modDf02 %>%
ggplot(aes(x = estimate)) + geom_histogram() +
scale_x_continuous(breaks = seq(from = -4, to = 2, by = 1))
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
There are some things that are ore abundant in the large particles
So Many ASVs. How do I ID the ones I want?
I kind of want to look at average concentrations (or sums)
SlopesAndMeans <- nonSpikes20 %>%
#filter(Kingdom == "Bacteria") %>%
group_by(ASV) %>%
summarise(meanCopies = mean(copiesPerL)) %>%
left_join(modDf02, by = "ASV")
SlopesAndMeans %>% head()
SlopesAndMeans %>%
filter(estimate >= 0) %>%
ggplot(aes(x = estimate, y = meanCopies, label = ASV)) +
geom_point() +
geom_label_repel()
Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ASV 13 looks promising
taxa %>%
filter(ASV %in% c("ASV_13", "ASV_23", "ASV_58"))
The big stuff are all cyanos! Huh.
Targets <- SlopesAndMeans %>% filter(estimate > 0, meanCopies > 1e5) %>% pull(ASV)
taxa %>%
filter(ASV %in% Targets)
Horses <- SlopesAndMeans %>%
left_join(taxa, by = "ASV") %>%
filter(estimate > 0, meanCopies > 0e5) %>%
arrange(-meanCopies)
Horses
Ok. Big stuff are cyanos and Planctomycetes, and apparently Spiders. Also apparently some proteos of different stripes. Rhodobacter are cool though.
nonSpikes20 %>%
left_join(SlopesAndMeans, by = "ASV") %>%
filter(estimate > 0, meanCopies > 1e5, Depth != "Oxy") %>%
arrange(-meanCopies, Size_Class) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class, fill = as.factor(Station), shape = as.factor(Station))) +
scale_y_log10nice() + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Depth ~ Tag) +
geom_point(size = 2) +
geom_path(aes(color = as.factor(Station))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
nonSpikes20 %>%
left_join(SlopesAndMeans, by = "ASV") %>%
filter(estimate > 0, meanCopies > 1e5, Depth != "Oxy") %>%
arrange(-meanCopies, Size_Class) %>%
filter(ASV == "ASV_7") %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class, fill = as.factor(Station), shape = as.factor(Station))) +
scale_y_log10nice() + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Depth ~ Tag) +
geom_point(size = 2) +
geom_path(aes(color = as.factor(Station))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
nonSpikes20 %>%
left_join(SlopesAndMeans, by = "ASV") %>%
filter(estimate > 0, meanCopies < 1e5, Depth != "Oxy") %>%
arrange(-meanCopies, Size_Class) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class, fill = as.factor(Station), shape = as.factor(Station))) +
scale_y_log10nice() + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Depth ~ Tag) +
geom_point(size = 2) +
geom_path(aes(color = as.factor(Station))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90))
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
Lets look at these!
I like, as examples Paracoccus (a Rhodobacter) -ASV_459 Synechococcus_CC9902 – ASV_23 SAR11_Clade – ASV_3
nonSpikes20 %>%
filter(ASV %in% c("ASV_3", "ASV_23", "ASV_459"), Depth != "Oxy") %>%
arrange(Size_Class) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class, fill = as.factor(Station), shape = as.factor(Station))) +
scale_y_log10nice(omag = seq(-10, 20, by = 2)) + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Depth ~ Tag) +
geom_point(size = 3) +
geom_path(aes(color = as.factor(Station))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_viridis_d(option = "plasma") +
scale_color_viridis_d(option = "plasma") +
theme_bw(base_size = 14) +
theme(axis.text.x = element_text(angle = 90), legend.position = "none") +
labs(y = "16s Copies/L/mm", x = expression(paste("Particle Size (", mu, "m)")), fill = "Station", color = "Station", shape = "Station")
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
hgroups_ants <- cutree(sclust, k = 50)
nonSpikes20A <- nonSpikes20 %>% left_join(
tibble(ASV = names(hgroups_ants), cluster = hgroups_ants), by = "ASV")
# ASVsToView <- nonSpikes20A %>%
# filter(Kingdom == "Bacteria") %>%
# group_by(ASV, cluster) %>%
# summarise(meanCopies = mean(copiesPerL)) %>%
# group_by(cluster) %>%
# summarize(topASV = ASV[which.max(meanCopies)])
ClusterSumsA <- nonSpikes20A %>%
group_by(Station, Depth, cluster, Size_Class, Bin_Size) %>%
summarise(copiesPerL = sum(copiesPerL))
`summarise()` has grouped output by 'Station', 'Depth', 'cluster', 'Size_Class'. You can override using the `.groups` argument.
HugeSurfacePlot <- nonSpikes20A %>%
filter(Depth == "Surface") %>%
arrange(Bin_Size) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class)) +
facet_grid(cluster ~ Station) +
scale_y_log10nice(omag = seq(-10, 20, by = 4)) + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
geom_path(alpha = 0.5, aes(group = ASV)) +
theme_bw(base_size = 14) +
theme(axis.text.x = element_text(angle = 90), legend.position = "none") +
labs(y = "16s Copies/L/mm", x = expression(paste("Particle Size (", mu, "m)")), fill = "Station", color = "Station", shape = "Station") +
geom_path(color = "red", data = ClusterSumsA %>% filter(Depth == "Surface")) # Add sums
ggsave("HugeSurfacePlot.pdf", HugeSurfacePlot, height = 72, width = 8, limitsize = FALSE)
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
I think we’re using “intercept” in the clustering information somehow. Which I don’t particularly want. But the clustering does happen by correlation. Super strange. Maybe the correlation isn’t the way I think it is?
MinimalSurfacePlot <- nonSpikes20A %>%
filter(Depth == "Surface", cluster %in% c(1, 7, 15)) %>%
arrange(Bin_Size) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class)) +
facet_grid(cluster~ Station) +
scale_y_log10nice(omag = seq(-10, 20, by = 2)) + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
geom_path(alpha = 0.5, aes(group = ASV)) +
theme_bw(base_size = 14) +
theme(axis.text.x = element_text(angle = 90), legend.position = "none") +
labs(y = "16s Copies/L/mm", x = expression(paste("Particle Size (", mu, "m)")), fill = "Station", color = "Station", shape = "Station") +
geom_path(color = "red", data = ClusterSumsA %>% filter(Depth == "Surface", cluster %in% c(1, 7, 15))) # Add sums
ggsave("MinimalSurfacePlot.pdf", MinimalSurfacePlot, height = 11, width = 8, limitsize = FALSE)
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
MinimalSurfacePlot
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
MinimalClusterPlot <- nonSpikes20A %>%
filter(Depth != "Oxy", cluster %in% c(1, 7, 15)) %>%
arrange(Bin_Size) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class)) +
facet_grid(cluster + Depth ~ Station) +
scale_y_log10nice(omag = seq(-10, 20, by = 2)) + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
geom_path(alpha = 0.5, aes(group = ASV)) +
theme_bw(base_size = 14) +
theme(axis.text.x = element_text(angle = 90), legend.position = "none") +
labs(y = "16s Copies/L/mm", x = expression(paste("Particle Size (", mu, "m)")), fill = "Station", color = "Station", shape = "Station") +
geom_path(color = "red", data = ClusterSumsA %>% filter(Depth != "Oxy", cluster %in% c(1, 7, 15))) # Add sums
ggsave("MinimalClusterPlot.pdf", MinimalClusterPlot, height = 11, width = 8, limitsize = FALSE)
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
ExampleTaxa <- nonSpikes20 %>%
filter(ASV %in% c("ASV_3", "ASV_23", "ASV_459")) %>%
filter(Station %in% c("3.3", "4.3")) %>%
mutate(Depth = recode(Depth, Oxy = "Oxycline")) %>%
arrange(Size_Class) %>%
ggplot(aes(y = copiesPerL/Bin_Size, x = Size_Class, fill = as.factor(Depth), shape = as.factor(Depth))) +
scale_y_log10nice(omag = seq(-10, 20, by = 2)) + scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
facet_grid(Station ~ Tag) +
geom_point(size = 4) +
geom_path(aes(color = as.factor(Depth))) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_manual(values = c("green", "blue", "black")) +
scale_color_manual(values = c("green", "blue", "black")) +
theme_bw(base_size = 14) +
theme(axis.text.x = element_text(angle = 90), legend.position = "right") +
labs(y = "16s Copies/L/mm", x = expression(paste("Particle Size (", mu, "m)")), fill = "Station", color = "Station", shape = "Station")
ExampleTaxa
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
abunPlot0 <- microbialAbundance %>%
filter(is.finite(MassperLiter)) %>%
#filter(Depth %in% c("Surface", "Bottom")) %>%
filter(Station %in% c(3.3, 4.3)) %>%
mutate(Depth = recode(Depth, Oxy = "Oxycline")) %>%
mutate(copiesPerMg = copiesPerL / MassperLiter) %>%
ggplot(aes(x = Size_Class, y = copiesPerMg, shape = as.factor(Depth), fill = as.factor(Depth))) +
facet_wrap(~Station, ncol = 1) +
theme_bw(base_size = 16) +
geom_point(size = 4) +
#geom_path() +
geom_path(aes(color = as.factor(Depth))) +
scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
scale_y_log10nice() +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_manual(values = c("green", "blue", "black")) +
scale_color_manual(values = c("green", "blue", "black")) +
labs(y = "16s + 18s Genes / mg Particles", x = expression(paste("Particle Size (", mu, "m)")), color = "Depth", fill = "Depth", shape = "Depth") +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = .5),
axis.title.y = element_text(margin = unit(c(0, 0, 0, 0), "mm"), vjust = 0))
SairahLegend <- get_legend(abunPlot0)
abunPlot <- abunPlot0 + theme(legend.position = "none")
#abunPlot
PPLPlot <- microbialAbundance %>%
filter(Size_Class >= 1) %>%
#filter(Depth %in% c("Surface", "Bottom")) %>%
filter(Station %in% c(3.3, 4.3)) %>%
ggplot(aes(x = Size_Class, y = ParticlesPerLiter, shape = as.factor(Depth), fill = as.factor(Depth))) +
facet_wrap(~Station, ncol = 1) +
theme_bw(base_size = 16) +
geom_point(size = 4) +
#geom_path() +
geom_path(aes(color = as.factor(Depth))) +
scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
scale_y_log10nice(omag=seq(-10,20, by = 2)) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_manual(values = c("green", "blue", "black")) +
scale_color_manual(values = c("green", "blue", "black")) +
labs(y = "Particles/L/mm", x = expression(paste("Particle Size (", mu, "m)"))) +
theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = .5),
axis.title.y = element_text(margin = unit(c(0, 0, 0, 0), "mm"), vjust = 0))
#PPLPlot
ParticleMassPlot <- microbialAbundance %>%
filter(Size_Class >= 1) %>%
#filter(Depth %in% c("Surface", "Bottom")) %>%
filter(Station %in% c(3.3, 4.3)) %>%
ggplot(aes(x = Size_Class, y = MassperLiter/ParticlesPerLiter, shape = as.factor(Depth), fill = as.factor(Depth))) +
facet_wrap(~Station, ncol = 1) +
theme_bw(base_size = 16) +
geom_point(size = 4) +
#geom_path() +
geom_path(aes(color = as.factor(Depth))) +
scale_x_log10(breaks = my_sizes, labels = as.character(my_sizes)) +
scale_y_log10nice(omag = seq(-10, 20, by = 2)) +
scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_manual(values = c("green", "blue", "black")) +
scale_color_manual(values = c("green", "blue", "black")) +
labs(y = "Dry Mass (mg) / Particle", x = expression(paste("Particle Size (", mu, "m)"))) +
theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = .5),
axis.title.y = element_text(margin = unit(c(0, 0, 0, 0), "mm"), vjust = .5))
#ParticleMassPlot
x.grob <- textGrob(expression(paste("Particle Size (", mu, "m)")),
gp=gpar(fontsize=16))
combinedPlot <- plot_grid(PPLPlot, ParticleMassPlot, abunPlot, nrow = 1, labels = c("A", "B", "C"))
meta2 <- grid.arrange(arrangeGrob(combinedPlot, bottom = x.grob), right = SairahLegend)